Factor Analysis with Cognitive Age Delta Correcting for Years of Education
Iñigo Tellaetxe Elorriaga
2025-10-10
# Clear workspace
rm(list = ls(all.names = TRUE))
# Load libraries
library(ggplot2); library(dplyr); library(sjPlot);library(metafor); library(ggpubr); library(tools); library(readxl); library(purrr); library(ggstatsplot)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## Attaching package: 'sjPlot'## The following object is masked from 'package:ggplot2':
##
## set_theme## Loading required package: Matrix## Loading required package: metadat## Loading required package: numDeriv##
## Loading the 'metafor' package (version 4.8-0). For an
## introduction to the package please type: help(metafor)## Registered S3 methods overwritten by 'backports':
## method from
## as.character.Rconcordance tools
## print.Rconcordance tools## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167# Load functions
source('./regression_analysis.R')## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectknitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
theme_set(theme_bw())
graph_th <- theme(
text = element_text(family = "Times New Roman", size = 12),
axis.text = element_text(family = "Times New Roman", size = 12),
axis.title = element_text(family = "Times New Roman", size = 12),
strip.text = element_text(family = "Times New Roman", size = 8),
plot.margin = unit(c(0.2, 0.2, 0.2, 0.2), "cm"),
axis.title.y = element_text(margin = margin(r = 2, unit = "mm")),
axis.title.x = element_text(margin = margin(t = 2, unit = "mm"))
) Compare the deltas of the three groups
## NULLDEMOGRAPHICS
##
## Pearson's Chi-squared test
##
## data: sex_table
## X-squared = 1.3822, df = 2, p-value = 0.501##
## Kruskal-Wallis rank sum test
##
## data: educ_years by group
## Kruskal-Wallis chi-squared = 0.33112, df = 2, p-value = 0.8474##
## Kruskal-Wallis rank sum test
##
## data: age by group
## Kruskal-Wallis chi-squared = 14.385, df = 2, p-value = 0.0007523##
## Kruskal-Wallis rank sum test
##
## data: P1_APOE4_load by group
## Kruskal-Wallis chi-squared = 8.4406, df = 2, p-value = 0.01469##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(data_explore$P1_APOE4_load) and data_explore$group
##
## CU A- V- CU A- V+
## CU A- V+ 0.954 -
## CU A+ V- 0.012 0.161
##
## P value adjustment method: fdr## n age_mean age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 140 55.77143 6.37028 13.87857 3.595496 79 61 29## n age_mean age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 23 60.78261 8.382647 13.30435 3.547532 11 12 11## n age_mean age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 14 61.35714 7.406932 14.07143 3.561855 6 8 3Factor analysis
Let’s check if the Cognitive Age Delta is associated with the Education Years, because we tried to remove the effect of this variable in the features that were used to compute the CADs. The associations now should be weaker (smaller correlations and thus smaller effect size). Now, years of educations should in principle not be the variable that explains most variance
(Do not be fooled, these relations are NOT significant!!!)
We should check how informative sex, APOE4 load, and education years are of CAD all together, independent of each group.
What happens when we check the covariate effects by group? We know that education years has an effect, but what about sex and APOE4 load?
Covariate effects in CAD, in CU A- V- participants
##
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std,
## family = gaussian(), data = factor_data_cu)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05621 0.44713 0.126 0.9002
## educ_years_std 0.61917 0.29491 2.099 0.0376 *
## sex 0.03797 0.59426 0.064 0.9491
## P1_APOE4_load_std 0.04755 0.32534 0.146 0.8840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.13649)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1650.6 on 136 degrees of freedom
## AIC: 752.71
##
## Number of Fisher Scoring iterations: 2Covariate effects in CAD, in CU A+ V- participants
##
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std,
## family = gaussian(), data = factor_data_cu_a_pos_vasc_neg)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15764 1.26858 0.124 0.902
## educ_years_std 0.11121 0.84179 0.132 0.896
## sex 0.52161 1.67309 0.312 0.759
## P1_APOE4_load_std -0.09074 0.64739 -0.140 0.890
##
## (Dispersion parameter for gaussian family taken to be 14.88315)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 282.78 on 19 degrees of freedom
## AIC: 132.98
##
## Number of Fisher Scoring iterations: 2Covariate effects in CAD, in CU A- V+ participants
##
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std,
## family = gaussian(), data = factor_data_cu_a_neg_vasc_pos)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.19178 0.87020 1.370 0.201
## educ_years_std -0.44772 0.67735 -0.661 0.524
## sex -1.87128 1.33543 -1.401 0.191
## P1_APOE4_load_std -0.09714 0.72330 -0.134 0.896
##
## (Dispersion parameter for gaussian family taken to be 5.661516)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 56.615 on 10 degrees of freedom
## AIC: 69.291
##
## Number of Fisher Scoring iterations: 2No associations of CAD with education years controling for sex and APOE4 load.
Motor Scales
CU A- V-
##
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29522 0.254 0.800
## TINETTI_EQUILIBRIO_std 0.38420 0.29628 1.297 0.197
##
## (Dispersion parameter for gaussian family taken to be 12.20187)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1683.9 on 138 degrees of freedom
## AIC: 751.51
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TINETTI_MARCHA_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29700 0.253 0.801
## TINETTI_MARCHA_std -0.03942 0.29806 -0.132 0.895
##
## (Dispersion parameter for gaussian family taken to be 12.34898)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.2 on 138 degrees of freedom
## AIC: 753.19
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29594 0.254 0.800
## UPDRS_PUNTUACIONTOTAL_std 0.29812 0.29700 1.004 0.317
##
## (Dispersion parameter for gaussian family taken to be 12.26103)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1692.0 on 138 degrees of freedom
## AIC: 752.19
##
## Number of Fisher Scoring iterations: 2
No associations below statistical significance, thus, we do not correct
for covariates.
CU A+ V-
##
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7461 0.449 0.658
## TINETTI_EQUILIBRIO_std -0.8576 0.7629 -1.124 0.274
##
## (Dispersion parameter for gaussian family taken to be 12.8044)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 268.89 on 21 degrees of freedom
## AIC: 127.82
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TINETTI_MARCHA_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7195 0.465 0.646
## TINETTI_MARCHA_std -1.2618 0.7357 -1.715 0.101
##
## (Dispersion parameter for gaussian family taken to be 11.90691)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 250.05 on 21 degrees of freedom
## AIC: 126.15
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7664 0.437 0.667
## UPDRS_PUNTUACIONTOTAL_std 0.2466 0.7837 0.315 0.756
##
## (Dispersion parameter for gaussian family taken to be 13.51116)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 283.73 on 21 degrees of freedom
## AIC: 129.06
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO, family = gaussian(),
## data = data_motor)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6206 0.6 0.559
## TINETTI_EQUILIBRIO NA NA NA NA
##
## (Dispersion parameter for gaussian family taken to be 5.392821)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.107 on 13 degrees of freedom
## AIC: 66.284
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TINETTI_MARCHA, family = gaussian(), data = data_motor)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6206 0.6 0.559
## TINETTI_MARCHA NA NA NA NA
##
## (Dispersion parameter for gaussian family taken to be 5.392821)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.107 on 13 degrees of freedom
## AIC: 66.284
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(),
## data = data_motor)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2495 0.6852 0.364 0.723
## UPDRS_PUNTUACIONTOTAL_std 0.1358 0.7132 0.190 0.852
##
## (Dispersion parameter for gaussian family taken to be 6.104043)
##
## Null deviance: 67.366 on 12 degrees of freedom
## Residual deviance: 67.144 on 11 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 64.237
##
## Number of Fisher Scoring iterations: 2
No associations below statistical significance, thus, we do not correct
for covariates.
Takeaway
No significant associations found.
Neurological History
CU A- V-
##
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08245 0.37677 0.219 0.827
## NEUROL_PRSNCE_Neu13 -0.01938 0.61236 -0.032 0.975
##
## (Dispersion parameter for gaussian family taken to be 12.35046)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.4 on 138 degrees of freedom
## AIC: 753.21
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29595 0.254 0.8
## CVADIS_PRSNCE_Neu13 NA NA NA NA
##
## (Dispersion parameter for gaussian family taken to be 12.2617)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.4 on 139 degrees of freedom
## AIC: 751.21
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07356 0.31634 0.233 0.816
## TRBINJ_PRSNCE_Neu13 -0.20261 1.02319 -0.198 0.843
##
## (Dispersion parameter for gaussian family taken to be 12.30898)
##
## Null deviance: 1649.9 on 135 degrees of freedom
## Residual deviance: 1649.4 on 134 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 731.34
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05585 1.05995 0.053 0.958
## NEUROL_PRSNCE_Neu13 0.58348 1.53268 0.381 0.707
##
## (Dispersion parameter for gaussian family taken to be 13.48185)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 283.12 on 21 degrees of freedom
## AIC: 129.01
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0003561 0.7031791 0.001 1.000
## CVADIS_PRSNCE_Neu13 7.6945382 3.3723286 2.282 0.033 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10.87814)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 228.44 on 21 degrees of freedom
## AIC: 124.07
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3142 0.8433 0.373 0.714
## TRBINJ_PRSNCE_Neu13 -2.3415 2.7326 -0.857 0.402
##
## (Dispersion parameter for gaussian family taken to be 13.51222)
##
## Null deviance: 266.65 on 20 degrees of freedom
## Residual deviance: 256.73 on 19 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 118.17
##
## Number of Fisher Scoring iterations: 2
### CU A+ V- correcting for sex, education years, and APOE4 load
##
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13 + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.27472 1.64541 -0.167 0.869
## NEUROL_PRSNCE_Neu13 0.72403 1.69596 0.427 0.675
## sex 0.67882 1.74949 0.388 0.703
## educ_years_std 0.07486 0.86471 0.087 0.932
## P1_APOE4_load_std -0.08213 0.66209 -0.124 0.903
##
## (Dispersion parameter for gaussian family taken to be 15.55252)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 279.95 on 18 degrees of freedom
## AIC: 134.75
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13 + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9812 1.2377 -0.793 0.438
## CVADIS_PRSNCE_Neu13 8.9827 3.8126 2.356 0.030 *
## sex 1.4709 1.5558 0.945 0.357
## educ_years_std -0.2225 0.7692 -0.289 0.776
## P1_APOE4_load_std 0.3101 0.6059 0.512 0.615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.00707)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 216.13 on 18 degrees of freedom
## AIC: 128.8
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13 + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08396 1.42155 0.059 0.954
## TRBINJ_PRSNCE_Neu13 -3.09464 3.35508 -0.922 0.370
## sex 1.01496 1.74723 0.581 0.569
## educ_years_std 0.47704 1.00508 0.475 0.641
## P1_APOE4_load_std -0.25324 0.66783 -0.379 0.710
##
## (Dispersion parameter for gaussian family taken to be 15.32214)
##
## Null deviance: 266.65 on 20 degrees of freedom
## Residual deviance: 245.15 on 16 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 123.2
##
## Number of Fisher Scoring iterations: 2## [1] FALSE FALSE FALSE
CU A- V+
##
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41233 1.08085 0.381 0.710
## NEUROL_PRSNCE_Neu13 -0.06243 1.34806 -0.046 0.964
##
## (Dispersion parameter for gaussian family taken to be 5.841179)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.094 on 12 degrees of freedom
## AIC: 68.281
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1710 0.6806 0.251 0.806
## CVADIS_PRSNCE_Neu13 1.4084 1.8007 0.782 0.449
##
## (Dispersion parameter for gaussian family taken to be 5.558847)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 66.706 on 12 degrees of freedom
## AIC: 67.588
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(),
## data = delta_neuro_history)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2351 0.7775 0.302 0.768
## TRBINJ_PRSNCE_Neu13 0.9754 1.6186 0.603 0.559
##
## (Dispersion parameter for gaussian family taken to be 6.045545)
##
## Null deviance: 68.697 on 12 degrees of freedom
## Residual deviance: 66.501 on 11 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 64.112
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
Sleep Variables
CU A- V-
##
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05687 0.29936 0.190 0.850
## PSQITT_C1TTLS_Nut901 0.28632 0.30046 0.953 0.342
##
## (Dispersion parameter for gaussian family taken to be 12.36745)
##
## Null deviance: 1693.2 on 137 degrees of freedom
## Residual deviance: 1682.0 on 136 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 742.69
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05687 0.29969 0.190 0.850
## PSQITT_C5TTLS_Nut901 -0.23532 0.30078 -0.782 0.435
##
## (Dispersion parameter for gaussian family taken to be 12.39424)
##
## Null deviance: 1693.2 on 137 degrees of freedom
## Residual deviance: 1685.6 on 136 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 742.99
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05687 0.29939 0.190 0.850
## PSQITT_PSQITT_Nut901 0.28299 0.30048 0.942 0.348
##
## (Dispersion parameter for gaussian family taken to be 12.36935)
##
## Null deviance: 1693.2 on 137 degrees of freedom
## Residual deviance: 1682.2 on 136 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 742.71
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7677 0.436 0.667
## PSQITT_C1TTLS_Nut901 -0.1378 0.7849 -0.176 0.862
##
## (Dispersion parameter for gaussian family taken to be 13.55499)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 284.65 on 21 degrees of freedom
## AIC: 129.13
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7634 0.439 0.665
## PSQITT_C5TTLS_Nut901 -0.4021 0.7806 -0.515 0.612
##
## (Dispersion parameter for gaussian family taken to be 13.40548)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 281.52 on 21 degrees of freedom
## AIC: 128.88
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7606 0.440 0.664
## PSQITT_PSQITT_Nut901 -0.5080 0.7777 -0.653 0.521
##
## (Dispersion parameter for gaussian family taken to be 13.30448)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 279.39 on 21 degrees of freedom
## AIC: 128.71
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5504 0.676 0.5117
## PSQITT_C1TTLS_Nut901 1.2159 0.5711 2.129 0.0547 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.240484)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 50.886 on 12 degrees of freedom
## AIC: 63.798
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5720 0.651 0.527
## PSQITT_C5TTLS_Nut901 1.0795 0.5935 1.819 0.094 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.579834)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 54.958 on 12 degrees of freedom
## AIC: 64.875
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(),
## data = delta_sleep)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5852 0.636 0.537
## PSQITT_PSQITT_Nut901 0.9837 0.6073 1.620 0.131
##
## (Dispersion parameter for gaussian family taken to be 4.794019)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 57.528 on 12 degrees of freedom
## AIC: 65.515
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
MFE_ADCS_ADL
CU A- V-
##
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29683 0.253 0.801
## P1_MFE_TotalScore -0.12228 0.29790 -0.410 0.682
##
## (Dispersion parameter for gaussian family taken to be 12.33549)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1702.3 on 138 degrees of freedom
## AIC: 753.04
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29655 0.253 0.80
## P1_ADLAUT_SCORE9_Neu213 -0.19651 0.29761 -0.660 0.51
##
## (Dispersion parameter for gaussian family taken to be 12.31165)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1699.0 on 138 degrees of freedom
## AIC: 752.76
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1029 0.2980 0.345 0.730
## P1_ADLINF_SCORE9_Neu214 -0.3285 0.2990 -1.098 0.274
##
## (Dispersion parameter for gaussian family taken to be 12.1621)
##
## Null deviance: 1656.6 on 136 degrees of freedom
## Residual deviance: 1641.9 on 135 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 735.04
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7499 0.447 0.660
## P1_MFE_TotalScore 0.7827 0.7667 1.021 0.319
##
## (Dispersion parameter for gaussian family taken to be 12.93314)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 271.60 on 21 degrees of freedom
## AIC: 128.05
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7641 0.438 0.666
## P1_ADLAUT_SCORE9_Neu213 -0.3733 0.7813 -0.478 0.638
##
## (Dispersion parameter for gaussian family taken to be 13.42891)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 282.01 on 21 degrees of freedom
## AIC: 128.92
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3387 0.7811 0.434 0.669
## P1_ADLINF_SCORE9_Neu214 0.8900 0.7994 1.113 0.279
##
## (Dispersion parameter for gaussian family taken to be 13.42148)
##
## Null deviance: 285.07 on 21 degrees of freedom
## Residual deviance: 268.43 on 20 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 123.47
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6198 0.601 0.559
## P1_MFE_TotalScore 0.6550 0.6432 1.018 0.329
##
## (Dispersion parameter for gaussian family taken to be 5.377434)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 64.529 on 12 degrees of freedom
## AIC: 67.123
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5529 0.673 0.5136
## P1_ADLAUT_SCORE9_Neu213 -1.2011 0.5737 -2.093 0.0582 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.27937)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 51.352 on 12 degrees of freedom
## AIC: 63.925
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(),
## data = delta_mfe)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6430 0.579 0.573
## P1_ADLINF_SCORE9_Neu214 0.2243 0.6672 0.336 0.743
##
## (Dispersion parameter for gaussian family taken to be 5.787733)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 69.453 on 12 degrees of freedom
## AIC: 68.153
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
Cardiovascular Parameters
CU A- V-
##
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2706 0.3193 0.848 0.398
## CARDIO_PRSNCE_Neu12 -1.3032 0.8244 -1.581 0.116
##
## (Dispersion parameter for gaussian family taken to be 12.13088)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1674.1 on 138 degrees of freedom
## AIC: 750.69
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1513 0.2970 0.509 0.6113
## IHDISE_PRSNCE_Neu12 -3.5529 2.0287 -1.751 0.0821 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.08201)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1667.3 on 138 degrees of freedom
## AIC: 750.13
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4352 0.9506 0.458 0.652
## CARDIO_PRSNCE_Neu12 -0.2883 1.6118 -0.179 0.860
##
## (Dispersion parameter for gaussian family taken to be 13.55423)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 284.64 on 21 degrees of freedom
## AIC: 129.13
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33267 0.78552 0.424 0.676
## IHDISE_PRSNCE_Neu12 0.05139 3.76720 0.014 0.989
##
## (Dispersion parameter for gaussian family taken to be 13.57477)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 285.07 on 21 degrees of freedom
## AIC: 129.17
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6206 0.6 0.559
## CARDIO_PRSNCE_Neu12 NA NA NA NA
##
## (Dispersion parameter for gaussian family taken to be 5.392821)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.107 on 13 degrees of freedom
## AIC: 66.284
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(),
## data = delta_cardio)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6206 0.6 0.559
## IHDISE_PRSNCE_Neu12 NA NA NA NA
##
## (Dispersion parameter for gaussian family taken to be 5.392821)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.107 on 13 degrees of freedom
## AIC: 66.284
##
## Number of Fisher Scoring iterations: 2Takeaway
No significant associations found in the sample.
Neurobehavioral Parameters
CU A- V-
##
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29667 0.253 0.800
## BPSHAD_HADANX_Neu207 0.16976 0.29773 0.570 0.569
##
## (Dispersion parameter for gaussian family taken to be 12.32152)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1700.4 on 138 degrees of freedom
## AIC: 752.88
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29701 0.253 0.801
## BPSHAD_HADEPR_Neu207 0.02026 0.29808 0.068 0.946
##
## (Dispersion parameter for gaussian family taken to be 12.35013)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.3 on 138 degrees of freedom
## AIC: 753.2
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1029 0.2988 0.344 0.731
## BPSNPI_NPITOT_Neu209 0.1958 0.2999 0.653 0.515
##
## (Dispersion parameter for gaussian family taken to be 12.23218)
##
## Null deviance: 1656.6 on 136 degrees of freedom
## Residual deviance: 1651.3 on 135 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 735.83
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12265 0.29525 0.415 0.678
## BPSTRS_PSQZSC_Neu210 0.05455 0.29631 0.184 0.854
##
## (Dispersion parameter for gaussian family taken to be 12.11671)
##
## Null deviance: 1660.4 on 138 degrees of freedom
## Residual deviance: 1660.0 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 745.2
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7665 0.437 0.667
## BPSHAD_HADANX_Neu207 0.2411 0.7838 0.308 0.761
##
## (Dispersion parameter for gaussian family taken to be 13.51398)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 283.79 on 21 degrees of freedom
## AIC: 129.06
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7647 0.438 0.666
## BPSHAD_HADEPR_Neu207 0.3463 0.7819 0.443 0.662
##
## (Dispersion parameter for gaussian family taken to be 13.44923)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 282.43 on 21 degrees of freedom
## AIC: 128.95
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1490 0.7772 0.192 0.850
## BPSNPI_NPITOT_Neu209 1.1708 0.7964 1.470 0.158
##
## (Dispersion parameter for gaussian family taken to be 12.68583)
##
## Null deviance: 268.45 on 20 degrees of freedom
## Residual deviance: 241.03 on 19 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 116.84
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7517 0.446 0.661
## BPSTRS_PSQZSC_Neu210 -0.7424 0.7686 -0.966 0.345
##
## (Dispersion parameter for gaussian family taken to be 12.99754)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 272.95 on 21 degrees of freedom
## AIC: 128.17
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6192 0.601 0.559
## BPSHAD_HADANX_Neu207 0.6618 0.6426 1.030 0.323
##
## (Dispersion parameter for gaussian family taken to be 5.367691)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 64.412 on 12 degrees of freedom
## AIC: 67.098
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5892 0.632 0.539
## BPSHAD_HADEPR_Neu207 0.9520 0.6114 1.557 0.145
##
## (Dispersion parameter for gaussian family taken to be 4.860293)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 58.324 on 12 degrees of freedom
## AIC: 65.708
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5657 0.658 0.5230
## BPSNPI_NPITOT_Neu209 1.1213 0.5870 1.910 0.0803 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.480152)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 53.762 on 12 degrees of freedom
## AIC: 64.567
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(),
## data = delta_neurobehav)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5672 0.656 0.5241
## BPSTRS_PSQZSC_Neu210 -1.1111 0.5887 -1.888 0.0835 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.504759)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 54.057 on 12 degrees of freedom
## AIC: 64.644
##
## Number of Fisher Scoring iterations: 2
Takeway
No significant associations found.
Vascular Parameters
CU A- V-
##
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29613 0.254 0.800
## P1_CVMSRS_SYSTBP_Nur201 -0.27055 0.29719 -0.910 0.364
##
## (Dispersion parameter for gaussian family taken to be 12.27682)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1694.2 on 138 degrees of freedom
## AIC: 752.37
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29621 0.254 0.800
## P1_CVMSRS_DIASBP_Nur201 -0.25834 0.29727 -0.869 0.386
##
## (Dispersion parameter for gaussian family taken to be 12.28333)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1695.1 on 138 degrees of freedom
## AIC: 752.44
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29372 0.256 0.7985
## P1_CVMSRS_HRTRTE_Nur201 0.52027 0.29477 1.765 0.0798 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.07791)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1666.8 on 138 degrees of freedom
## AIC: 750.08
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29641 0.253 0.800
## P1_CVMSRS_RTANKA_Nur201 0.22385 0.29747 0.752 0.453
##
## (Dispersion parameter for gaussian family taken to be 12.30008)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1697.4 on 138 degrees of freedom
## AIC: 752.63
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29473 0.255 0.799
## P1_CVMSRS_LTANKA_Nur201 -0.43353 0.29579 -1.466 0.145
##
## (Dispersion parameter for gaussian family taken to be 12.16123)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1678.3 on 138 degrees of freedom
## AIC: 751.04
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29667 0.253 0.800
## P1_ECG999_HRTRTE_Nur202 0.16843 0.29774 0.566 0.573
##
## (Dispersion parameter for gaussian family taken to be 12.32197)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1700.4 on 138 degrees of freedom
## AIC: 752.88
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7673 0.436 0.667
## P1_CVMSRS_SYSTBP_Nur201 0.1775 0.7846 0.226 0.823
##
## (Dispersion parameter for gaussian family taken to be 13.54187)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 284.38 on 21 degrees of freedom
## AIC: 129.11
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7522 0.445 0.661
## P1_CVMSRS_DIASBP_Nur201 -0.7323 0.7691 -0.952 0.352
##
## (Dispersion parameter for gaussian family taken to be 13.01306)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 273.27 on 21 degrees of freedom
## AIC: 128.2
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7495 0.447 0.660
## P1_CVMSRS_HRTRTE_Nur201 -0.7904 0.7664 -1.031 0.314
##
## (Dispersion parameter for gaussian family taken to be 12.92049)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 271.33 on 21 degrees of freedom
## AIC: 128.03
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7216 0.464 0.647
## P1_CVMSRS_RTANKA_Nur201 -1.2353 0.7378 -1.674 0.109
##
## (Dispersion parameter for gaussian family taken to be 11.9763)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 251.50 on 21 degrees of freedom
## AIC: 126.29
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7581 0.442 0.663
## P1_CVMSRS_LTANKA_Nur201 -0.5821 0.7752 -0.751 0.461
##
## (Dispersion parameter for gaussian family taken to be 13.21997)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 277.62 on 21 degrees of freedom
## AIC: 128.56
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0546 0.7150 0.076 0.940
## P1_ECG999_HRTRTE_Nur202 -0.9857 0.7318 -1.347 0.193
##
## (Dispersion parameter for gaussian family taken to be 11.2457)
##
## Null deviance: 245.32 on 21 degrees of freedom
## Residual deviance: 224.91 on 20 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 119.58
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6452 0.577 0.575
## P1_CVMSRS_SYSTBP_Nur201 0.1144 0.6696 0.171 0.867
##
## (Dispersion parameter for gaussian family taken to be 5.828034)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 69.936 on 12 degrees of freedom
## AIC: 68.25
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6345 0.587 0.568
## P1_CVMSRS_DIASBP_Nur201 0.4358 0.6585 0.662 0.521
##
## (Dispersion parameter for gaussian family taken to be 5.63652)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 67.638 on 12 degrees of freedom
## AIC: 67.782
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6447 0.577 0.574
## P1_CVMSRS_HRTRTE_Nur201 -0.1452 0.6691 -0.217 0.832
##
## (Dispersion parameter for gaussian family taken to be 5.819384)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 69.833 on 12 degrees of freedom
## AIC: 68.229
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6297 0.591 0.565
## P1_CVMSRS_RTANKA_Nur201 -0.5188 0.6534 -0.794 0.443
##
## (Dispersion parameter for gaussian family taken to be 5.550613)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 66.607 on 12 degrees of freedom
## AIC: 67.567
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6206 0.6 0.560
## P1_CVMSRS_LTANKA_Nur201 -0.6442 0.6441 -1.0 0.337
##
## (Dispersion parameter for gaussian family taken to be 5.392701)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 64.712 on 12 degrees of freedom
## AIC: 67.163
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(),
## data = delta_vascular)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6090 0.611 0.553
## P1_ECG999_HRTRTE_Nur202 -0.7742 0.6320 -1.225 0.244
##
## (Dispersion parameter for gaussian family taken to be 5.192883)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 62.315 on 12 degrees of freedom
## AIC: 66.634
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
Cognitive Reserve
CU A- V-
##
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07772 0.32907 0.236 0.814
## CRC_Total_std 0.26842 0.33060 0.812 0.419
##
## (Dispersion parameter for gaussian family taken to be 11.69501)
##
## Null deviance: 1247.4 on 107 degrees of freedom
## Residual deviance: 1239.7 on 106 degrees of freedom
## (32 observations deleted due to missingness)
## AIC: 576.06
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.06107 0.29109 0.210 0.83414
## P1_VOCBWS_TTLZSC_Nps102_std -0.79376 0.29214 -2.717 0.00744 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.77801)
##
## Null deviance: 1700.5 on 138 degrees of freedom
## Residual deviance: 1613.6 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 741.26
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29700 0.253 0.801
## P1_BILINGUISMO_std 0.03473 0.29807 0.117 0.907
##
## (Dispersion parameter for gaussian family taken to be 12.34933)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.2 on 138 degrees of freedom
## AIC: 753.19
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29698 0.253 0.801
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.05419 0.29805 -0.182 0.856
##
## (Dispersion parameter for gaussian family taken to be 12.34759)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1704.0 on 138 degrees of freedom
## AIC: 753.17
##
## Number of Fisher Scoring iterations: 2CU A- V- correcting for sex, educ_years, and APOE4 load
##
## Call:
## glm(formula = delta ~ CRC_Total_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1355 0.5042 -0.269 0.78866
## CRC_Total_std -0.4904 0.4156 -1.180 0.24071
## sex 0.2830 0.6623 0.427 0.67004
## educ_years_std 1.1990 0.4054 2.957 0.00385 **
## P1_APOE4_load_std 0.1420 0.3524 0.403 0.68783
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.04597)
##
## Null deviance: 1247.4 on 107 degrees of freedom
## Residual deviance: 1137.7 on 103 degrees of freedom
## (32 observations deleted due to missingness)
## AIC: 572.79
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35501 0.42714 0.831 0.407382
## P1_VOCBWS_TTLZSC_Nps102_std -1.39063 0.31992 -4.347 2.71e-05 ***
## sex -0.49802 0.57490 -0.866 0.387888
## educ_years_std 1.22090 0.31462 3.881 0.000163 ***
## P1_APOE4_load_std 0.03967 0.30707 0.129 0.897400
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10.78994)
##
## Null deviance: 1700.5 on 138 degrees of freedom
## Residual deviance: 1445.9 on 134 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 732
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05437 0.45011 0.121 0.9040
## P1_BILINGUISMO_std -0.01593 0.30039 -0.053 0.9578
## sex 0.04092 0.59904 0.068 0.9456
## educ_years_std 0.62058 0.29720 2.088 0.0387 *
## P1_APOE4_load_std 0.04586 0.32810 0.140 0.8890
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.22613)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1650.5 on 135 degrees of freedom
## AIC: 754.71
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std + sex +
## educ_years_std + P1_APOE4_load_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05581 0.44769 0.125 0.9010
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.25130 0.30858 -0.814 0.4169
## sex 0.03925 0.59500 0.066 0.9475
## educ_years_std 0.69019 0.30789 2.242 0.0266 *
## P1_APOE4_load_std 0.05338 0.32583 0.164 0.8701
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12.16662)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1642.5 on 135 degrees of freedom
## AIC: 754.03
##
## Number of Fisher Scoring iterations: 2## [1] 0.4814278103 0.0001084402 0.9577967919 0.5558294353
See the association between years of education and vocabulary.
See the association between delta and vocabulary.
CU A+ V-
##
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7824 0.7402 1.057 0.305
## CRC_Total_std -1.2171 0.7605 -1.601 0.128
##
## (Dispersion parameter for gaussian family taken to be 10.40935)
##
## Null deviance: 203.62 on 18 degrees of freedom
## Residual deviance: 176.96 on 17 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 102.32
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.6956 0.481 0.6352
## P1_VOCBWS_TTLZSC_Nps102_std -1.5283 0.7112 -2.149 0.0435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.12785)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 233.68 on 21 degrees of freedom
## AIC: 124.6
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7096 0.472 0.642
## P1_BILINGUISMO_std -1.3797 0.7255 -1.902 0.071 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.5808)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 243.20 on 21 degrees of freedom
## AIC: 125.51
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7396 0.453 0.655
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.9734 0.7563 -1.287 0.212
##
## (Dispersion parameter for gaussian family taken to be 12.58221)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 264.23 on 21 degrees of freedom
## AIC: 127.42
##
## Number of Fisher Scoring iterations: 2CU A+ V- correcting for sex, educ_years, and APOE4 load
##
## Call:
## glm(formula = delta ~ CRC_Total_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.18668 1.16257 1.021 0.3247
## CRC_Total_std -2.46616 0.94911 -2.598 0.0210 *
## sex 0.26870 1.48633 0.181 0.8591
## educ_years_std 2.24922 1.02008 2.205 0.0447 *
## P1_APOE4_load_std -0.07155 0.58923 -0.121 0.9051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9.379471)
##
## Null deviance: 203.62 on 18 degrees of freedom
## Residual deviance: 131.31 on 14 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 102.65
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2035 1.2152 0.990 0.3351
## P1_VOCBWS_TTLZSC_Nps102_std -2.0822 0.8676 -2.400 0.0274 *
## sex -0.9060 1.6101 -0.563 0.5806
## educ_years_std 0.8935 0.8203 1.089 0.2904
## P1_APOE4_load_std -0.4893 0.6023 -0.812 0.4272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.90155)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 214.23 on 18 degrees of freedom
## AIC: 128.6
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29237 1.20108 0.243 0.810
## P1_BILINGUISMO_std -1.45818 0.80545 -1.810 0.087 .
## sex 0.26439 1.58738 0.167 0.870
## educ_years_std 0.43071 0.81480 0.529 0.604
## P1_APOE4_load_std -0.02726 0.61276 -0.044 0.965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 13.29007)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 239.22 on 18 degrees of freedom
## AIC: 131.13
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std + sex +
## educ_years_std + P1_APOE4_load_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06812 1.23763 -0.055 0.957
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -1.36417 0.90734 -1.503 0.150
## sex 1.09978 1.66522 0.660 0.517
## educ_years_std 0.72413 0.91144 0.794 0.437
## P1_APOE4_load_std -0.01583 0.62890 -0.025 0.980
##
## (Dispersion parameter for gaussian family taken to be 13.95723)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 251.23 on 18 degrees of freedom
## AIC: 132.26
##
## Number of Fisher Scoring iterations: 2## [1] 0.05485583 0.05485583 0.11594744 0.15005737
CU A- V+
##
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6576 0.6069 1.084 0.307
## CRC_Total_std -1.0081 0.6366 -1.584 0.148
##
## (Dispersion parameter for gaussian family taken to be 4.052105)
##
## Null deviance: 46.631 on 10 degrees of freedom
## Residual deviance: 36.469 on 9 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 50.401
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37219 0.64586 0.576 0.575
## P1_VOCBWS_TTLZSC_Nps102_std -0.04584 0.67024 -0.068 0.947
##
## (Dispersion parameter for gaussian family taken to be 5.839946)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 70.079 on 12 degrees of freedom
## AIC: 68.278
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6363 0.585 0.569
## P1_BILINGUISMO_std -0.3998 0.6604 -0.605 0.556
##
## (Dispersion parameter for gaussian family taken to be 5.669086)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 68.029 on 12 degrees of freedom
## AIC: 67.863
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(),
## data = delta_cog_reserv)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5865 0.635 0.538
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.9731 0.6087 -1.599 0.136
##
## (Dispersion parameter for gaussian family taken to be 4.816348)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 57.796 on 12 degrees of freedom
## AIC: 65.58
##
## Number of Fisher Scoring iterations: 2
Takeaway
We see a strong association between delta and the vocabulary test Z score (P1_VOCBWS_TTLZSC_Nps102), when asking “What is the effect of vocabulary independent of education, sex, and APOE4 load?”. The vocabulary test is highly correlated to the years of education (they share variance), but because we corrected for this effect in our question, we are looking at the effect of residual variation in the vocabulary that education does not explain. The vocabulary score predicts delta even after removing the shared variance with education, so vocabulary captures something beyond educational effects.
Tobacco
CU A- V-
##
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29676 0.253 0.801
## SMOKNG_NUMCIG_Nut501_std -0.14577 0.29782 -0.489 0.625
##
## (Dispersion parameter for gaussian family taken to be 12.32915)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1701.4 on 138 degrees of freedom
## AIC: 752.96
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3193 0.5292 0.603 0.547
## SMOKNG_TYPSMK_Nut501 -0.3562 0.6391 -0.557 0.578
##
## (Dispersion parameter for gaussian family taken to be 12.32281)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1700.5 on 138 degrees of freedom
## AIC: 752.89
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7344 0.456 0.653
## SMOKNG_NUMCIG_Nut501_std 1.0572 0.7509 1.408 0.174
##
## (Dispersion parameter for gaussian family taken to be 12.40394)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 260.48 on 21 degrees of freedom
## AIC: 127.09
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5669 1.2796 -0.443 0.662
## SMOKNG_TYPSMK_Nut501 1.3828 1.5845 0.873 0.393
##
## (Dispersion parameter for gaussian family taken to be 13.0998)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 275.10 on 21 degrees of freedom
## AIC: 128.35
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6015 0.619 0.548
## SMOKNG_NUMCIG_Nut501_std 0.8469 0.6242 1.357 0.200
##
## (Dispersion parameter for gaussian family taken to be 5.065207)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 60.782 on 12 degrees of freedom
## AIC: 66.286
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(),
## data = delta_tobacco)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8284 0.8738 -0.948 0.3618
## SMOKNG_TYPSMK_Nut501 2.1010 1.1559 1.818 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.581073)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 54.973 on 12 degrees of freedom
## AIC: 64.879
##
## Number of Fisher Scoring iterations: 2
Takeaway
No statistically significant associations found.
Alcohol
CU A- V-
##
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2654 0.3903 0.68 0.498
## ALCOHL_TOTALS_Nut401 -0.1360 0.1815 -0.75 0.455
##
## (Dispersion parameter for gaussian family taken to be 12.30047)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1697.5 on 138 degrees of freedom
## AIC: 752.64
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3271 0.3822 0.856 0.394
## ALCH30_TOTALS_Nut401 -0.1433 0.1376 -1.041 0.300
##
## (Dispersion parameter for gaussian family taken to be 12.25425)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1691.1 on 138 degrees of freedom
## AIC: 752.11
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0194 0.9923 -1.027 0.3160
## ALCOHL_TOTALS_Nut401 0.7395 0.3801 1.946 0.0652 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.50177)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 241.54 on 21 degrees of freedom
## AIC: 125.36
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3123 0.8324 -0.375 0.711
## ALCH30_TOTALS_Nut401 0.2462 0.1550 1.588 0.127
##
## (Dispersion parameter for gaussian family taken to be 12.11892)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 254.50 on 21 degrees of freedom
## AIC: 126.56
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.0070 0.9584 -1.051 0.3141
## ALCOHL_TOTALS_Nut401 0.7213 0.4016 1.796 0.0977 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.604367)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 55.252 on 12 degrees of freedom
## AIC: 64.95
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(),
## data = delta_alcohol)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8025 0.7680 -1.045 0.3167
## ALCH30_TOTALS_Nut401 0.3453 0.1585 2.178 0.0501 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.186803)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 50.242 on 12 degrees of freedom
## AIC: 63.619
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
Diet
CU A- V-
##
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(),
## data = delta_diet)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.073462 0.303212 0.242 0.809
## MDAS13_NA13SC_Nut151_std -0.007234 0.304325 -0.024 0.981
##
## (Dispersion parameter for gaussian family taken to be 12.59545)
##
## Null deviance: 1700.4 on 136 degrees of freedom
## Residual deviance: 1700.4 on 135 degrees of freedom
## (3 observations deleted due to missingness)
## AIC: 739.84
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(),
## data = delta_diet)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7393 0.453 0.655
## MDAS13_NA13SC_Nut151_std -0.9786 0.7559 -1.295 0.210
##
## (Dispersion parameter for gaussian family taken to be 12.57159)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 264.00 on 21 degrees of freedom
## AIC: 127.4
##
## Number of Fisher Scoring iterations: 2
CU A- V+
##
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(),
## data = delta_diet)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6440 0.578 0.574
## MDAS13_NA13SC_Nut151_std -0.1826 0.6683 -0.273 0.789
##
## (Dispersion parameter for gaussian family taken to be 5.806113)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 69.673 on 12 degrees of freedom
## AIC: 68.197
##
## Number of Fisher Scoring iterations: 2
Takeaway
No significant associations found.
IPAQ & Sitting
CU A- V-
##
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07512 0.29603 0.254 0.800
## P1_IPAQMH_PATOTL_Nut701_std 0.28557 0.29709 0.961 0.338
##
## (Dispersion parameter for gaussian family taken to be 12.26841)
##
## Null deviance: 1704.4 on 139 degrees of freedom
## Residual deviance: 1693.0 on 138 degrees of freedom
## AIC: 752.27
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05171 0.29745 0.174 0.862
## SDNTMF_CMPUTR_Nut801_std -0.25407 0.29852 -0.851 0.396
##
## (Dispersion parameter for gaussian family taken to be 12.29786)
##
## Null deviance: 1693.7 on 138 degrees of freedom
## Residual deviance: 1684.8 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 747.26
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05171 0.29822 0.173 0.863
## SDNTMF_DRIVNG_Nut801_std 0.02273 0.29930 0.076 0.940
##
## (Dispersion parameter for gaussian family taken to be 12.36236)
##
## Null deviance: 1693.7 on 138 degrees of freedom
## Residual deviance: 1693.6 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 747.99
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05171 0.29822 0.173 0.863
## SDNTSS_CMPUTR_Nut801_std 0.02864 0.29930 0.096 0.924
##
## (Dispersion parameter for gaussian family taken to be 12.36205)
##
## Null deviance: 1693.7 on 138 degrees of freedom
## Residual deviance: 1693.6 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 747.98
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05171 0.29799 0.174 0.862
## SDNTSS_DRIVNG_Nut801_std -0.13983 0.29907 -0.468 0.641
##
## (Dispersion parameter for gaussian family taken to be 12.34319)
##
## Null deviance: 1693.7 on 138 degrees of freedom
## Residual deviance: 1691.0 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 747.77
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05171 0.29782 0.174 0.862
## SDNTTT_SITTOT_Nut801_std -0.18346 0.29890 -0.614 0.540
##
## (Dispersion parameter for gaussian family taken to be 12.32898)
##
## Null deviance: 1693.7 on 138 degrees of freedom
## Residual deviance: 1689.1 on 137 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 747.61
##
## Number of Fisher Scoring iterations: 2
CU A+ V-
##
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33490 0.76823 0.436 0.667
## P1_IPAQMH_PATOTL_Nut701_std -0.02642 0.78550 -0.034 0.973
##
## (Dispersion parameter for gaussian family taken to be 13.57416)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 285.06 on 21 degrees of freedom
## AIC: 129.17
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7001 0.478 0.6373
## SDNTMF_CMPUTR_Nut801_std -1.4822 0.7158 -2.071 0.0509 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.27347)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 236.74 on 21 degrees of freedom
## AIC: 124.9
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7656 0.437 0.666
## SDNTMF_DRIVNG_Nut801_std 0.2972 0.7828 0.380 0.708
##
## (Dispersion parameter for gaussian family taken to be 13.48234)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 283.13 on 21 degrees of freedom
## AIC: 129.01
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7456 0.449 0.658
## SDNTSS_CMPUTR_Nut801_std -0.8679 0.7623 -1.138 0.268
##
## (Dispersion parameter for gaussian family taken to be 12.78582)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 268.50 on 21 degrees of freedom
## AIC: 127.79
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.7486 0.447 0.659
## SDNTSS_DRIVNG_Nut801_std 0.8083 0.7655 1.056 0.303
##
## (Dispersion parameter for gaussian family taken to be 12.89046)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 270.70 on 21 degrees of freedom
## AIC: 127.98
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3349 0.6460 0.518 0.60956
## SDNTTT_SITTOT_Nut801_std -1.9486 0.6605 -2.950 0.00764 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 9.596946)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 201.54 on 21 degrees of freedom
## AIC: 121.19
##
## Number of Fisher Scoring iterations: 2
CU A+ V-: correcting for sex, educ_years, and APOE4_load
##
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.33490 0.76823 0.436 0.667
## P1_IPAQMH_PATOTL_Nut701_std -0.02642 0.78550 -0.034 0.973
##
## (Dispersion parameter for gaussian family taken to be 13.57416)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 285.06 on 21 degrees of freedom
## AIC: 129.17
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.08868 1.10292 0.080 0.9368
## SDNTMF_CMPUTR_Nut801_std -2.31786 0.86681 -2.674 0.0155 *
## sex 1.25166 1.47960 0.846 0.4087
## educ_years_std 1.42300 0.88090 1.615 0.1236
## P1_APOE4_load_std -0.21457 0.56459 -0.380 0.7084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.24359)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 202.38 on 18 degrees of freedom
## AIC: 127.29
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16911 1.30022 0.130 0.898
## SDNTMF_DRIVNG_Nut801_std 0.27072 0.85373 0.317 0.755
## sex 0.48121 1.71888 0.280 0.783
## educ_years_std 0.07039 0.87200 0.081 0.937
## P1_APOE4_load_std -0.08833 0.66332 -0.133 0.896
##
## (Dispersion parameter for gaussian family taken to be 15.62272)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 281.21 on 18 degrees of freedom
## AIC: 134.85
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.46931 1.30302 0.360 0.723
## SDNTSS_CMPUTR_Nut801_std -0.90005 0.87898 -1.024 0.319
## sex -0.06594 1.76673 -0.037 0.971
## educ_years_std 0.21531 0.84684 0.254 0.802
## P1_APOE4_load_std -0.11455 0.64698 -0.177 0.861
##
## (Dispersion parameter for gaussian family taken to be 14.84525)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 267.21 on 18 degrees of freedom
## AIC: 133.68
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.27504 1.30556 -0.211 0.836
## SDNTSS_DRIVNG_Nut801_std 1.06683 0.89192 1.196 0.247
## sex 1.27829 1.77127 0.722 0.480
## educ_years_std 0.31692 0.84999 0.373 0.714
## P1_APOE4_load_std 0.07985 0.65587 0.122 0.904
##
## (Dispersion parameter for gaussian family taken to be 14.55327)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 261.96 on 18 degrees of freedom
## AIC: 133.22
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std + sex + educ_years_std +
## P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1173 1.0762 0.109 0.91439
## SDNTTT_SITTOT_Nut801_std -2.1345 0.7363 -2.899 0.00957 **
## sex 0.3554 1.4204 0.250 0.80528
## educ_years_std 0.5669 0.7312 0.775 0.44824
## P1_APOE4_load_std 0.2259 0.5599 0.403 0.69138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 10.71008)
##
## Null deviance: 285.07 on 22 degrees of freedom
## Residual deviance: 192.78 on 18 degrees of freedom
## AIC: 126.17
##
## Number of Fisher Scoring iterations: 2
## [1] FALSE TRUE FALSE FALSE FALSE TRUE## [1] 0.97348445 0.04644656 0.90577434 0.47912444 0.47912444 0.04644656CU A- V+
##
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6022 0.618 0.548
## P1_IPAQMH_PATOTL_Nut701_std -0.8406 0.6249 -1.345 0.203
##
## (Dispersion parameter for gaussian family taken to be 5.076716)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 60.921 on 12 degrees of freedom
## AIC: 66.317
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6370 0.584 0.570
## SDNTMF_CMPUTR_Nut801_std -0.3854 0.6611 -0.583 0.571
##
## (Dispersion parameter for gaussian family taken to be 5.681346)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 68.176 on 12 degrees of freedom
## AIC: 67.893
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6182 0.602 0.558
## SDNTMF_DRIVNG_Nut801_std 0.6743 0.6415 1.051 0.314
##
## (Dispersion parameter for gaussian family taken to be 5.349711)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 64.197 on 12 degrees of freedom
## AIC: 67.051
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5774 0.645 0.531
## SDNTSS_CMPUTR_Nut801_std 1.0411 0.5992 1.737 0.108
##
## (Dispersion parameter for gaussian family taken to be 4.667939)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 56.015 on 12 degrees of freedom
## AIC: 65.142
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.5901 0.631 0.540
## SDNTSS_DRIVNG_Nut801_std 0.9445 0.6124 1.542 0.149
##
## (Dispersion parameter for gaussian family taken to be 4.875813)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 58.510 on 12 degrees of freedom
## AIC: 65.752
##
## Number of Fisher Scoring iterations: 2##
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(),
## data = delta_ipaq_sitting)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3722 0.6406 0.581 0.572
## SDNTTT_SITTOT_Nut801_std 0.2999 0.6648 0.451 0.660
##
## (Dispersion parameter for gaussian family taken to be 5.744769)
##
## Null deviance: 70.107 on 13 degrees of freedom
## Residual deviance: 68.937 on 12 degrees of freedom
## AIC: 68.048
##
## Number of Fisher Scoring iterations: 2
Takeaway
Significant negative association between sedentarism score and CAD in CU A+ V- group. Interpretation is not intuitive a priori “the more hours you sit, the less CAD you have (less cognitive aging)”. However, we see consistent associations with education years and CAD in the sense of “more education years means less cognitive aging”, and perhaps the sitting hours are employed in cognitively stimulating tasks for those who sit more.
Negative statistically significant effect of sitting hours in front of computer on delta in CU A+ V- group, even after correcting for sex, education years, and APOE4 load. “The more you sit in front of the computer from Monday to Friday, the less cognitive aging you have.”
Do those who sit more in front of the computer from Monday to Friday also have more education years?
Relationship between education years and sitting hours in front of the computer from Monday to Friday
CU A- V-
CU A+ V-
CU A+ V-
All groups
CSF signal association with CAD
CSF signals in the whole group.
CSF signals in the CU A- V- group
CSF signals in the CU A+ V- group
CSF signals in the CU A- V+ group
### Comparison of participants with CSF and no CSF
##
## Wilcoxon rank sum test with continuity correction
##
## data: data_csf$Age and data_no_csf$Age
## W = 21426, p-value = 0.4627
## alternative hypothesis: true location shift is not equal to 0##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: .
## X-squared = 2.0312, df = 1, p-value = 0.1541##
## Wilcoxon rank sum test with continuity correction
##
## data: data_csf$Educ_years and data_no_csf$Educ_years
## W = 19698, p-value = 0.4692
## alternative hypothesis: true location shift is not equal to 0##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: .
## X-squared = 0.0053023, df = 1, p-value = 0.942Ideas
After correcting for years of education, all possible associations are thrown off. The factor that is most associated with years of education, vocabulary, is also most associated with CADs, suggesting that our cognitive age delta captures this information, more intensely than any other cognitive domains.
The association of sedentarism and CADs in CU A+ V- could (perhaps?) be explained as noted above, stating that the sitting time is cognitively stimulating/meaningful. However this does not hold for the CU A- V- groups, what makes it more difficult to justify.
Also, it must be noted that our sample size is very reduced in all clinical groups, which makes drawing conclusions very challenging and a thing to do with utmost caution. Studies using this methodology typically use bigger sample sizes accross all their clinical groups, and particularly in the reference group (CU A- V- in our case).